Deep neural networks (DNNs) have been shown lack of robustness, as they are vulnerable to small perturbations on the inputs. This has led to safety concerns on applying DNNs to safety-critical domains. Several verification approaches based on constraint solving have been developed to automatically prove or disprove safety properties for DNNs. However, these approaches suffer from the scalability problem, i.e., only small DNNs can be handled. To deal with this, abstraction based approaches have been proposed, but are unfortunately facing the precision problem, i.e., the obtained bounds are often loose. In this paper, we focus on a variety of local robustness properties and a

(δ, ε)

-global robustness property of DNNs, and investigate novel strategies to combine the constraint solving and abstraction-based approaches to work with these properties:

•

We propose a method to verify local robustness, which improves a recent proposal of analyzing DNNs through the classic abstract interpretation technique, by a novel symbolic propagation technique. Specifically, the values of neurons are represented symbolically and propagated from the input layer to the output layer, on top of the underlying abstract domains. It achieves significantly higher precision and thus can prove more properties.

•

We propose a Lipschitz constant based verification framework. By utilising Lipschitz constants solved by semidefinite programming, we can prove global robustness of DNNs. We show how the Lipschitz constant can be tightened if it is restricted to small regions. A tightened Lipschitz constantcan be helpful in proving local robustness properties. Furthermore, a global Lipschitz constant can be used to accelerate batch local robustness verification, and thus support the verification of global robustness.

•

We show how the proposed abstract interpretation and Lipschitz constant based approaches can benefit from each other to obtain more precise results. Moreover, they can be also exploited and combined to improve constraints based approach.

We implement our methods in the tool PRODeep, and conduct detailed experimental results on several benchmarks

References

[1]

Anderson G, Pailoor S, Dillig I, Chaudhuri S (2019) Optimization and abstraction: a synergistic approach for analyzing neural network robustness. In: McKinley KS, Fisher K (eds), Proceedings of the 40th ACM SIGPLAN conference on programming language design and implementation, PLDI 2019, Phoenix, AZ, USA, June 22–26, 2019. ACM, pp 731–744

Abstract

References

References

Cited By

Recommendations

PRODeep: a platform for robustness verification of deep neural networks

Iterative Counter-Example Guided Robustness Verification for Neural Networks

OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Funding Sources

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations